Integrating Kalman Filter Inverse Modeling and Direct Sensitivities to Evaluate NOx Emission Inventory Biases Based on Satellite-Derived NO2 columns
Abstract
Regional air quality models are used to develop control strategies for reducing the ambient concentrations of harmful pollutants such as ozone and fine particulate matter. Regional models rely on detailed emission inventories, and these inventories still have a substantial amount of uncertainty despite continuing efforts for improvement. It is important to reduce nitrogen oxide (NOx) emission uncertainties, because these compounds regulate the levels of ozone in the troposphere, lead to formation of nitric acid, and impact the levels of hydroxyl radicals. A method was developed to constrain ground-level NOx emissions using an iterative Kalman filter inverse modeling technique and SCIAMACHY satellite observations of NO2. For the inverse modeling calculations, the relationship between emissions and modeled ambient concentrations was developed using sensitivities provided by the decoupled direct method in three dimensions (DDM-3D). The method was successfully tested using a controlled emissions scenario with a known synthetic solution (i.e., pseudodata test), and then applied to a summer 2004 episode where emissions of NOx were examined over a region covering the southeastern United States. The results indicate that while ground level NOx emission estimates in urban areas appear to be only slightly too high, rural emissions need to increase by a factor of two in order to produce the NO2 column densities observed by the satellite. However, over rural areas, the inverse is highly sensitive to NO2 concentrations in the upper troposphere where its origins are likely to be from lightning emissions of NO. Regional models often ignore lightning emissions, because these have been shown to have negligible impacts on boundary layer pollutant concentrations. But if satellite emissions are to be used to any extent in the context of regional inverse modeling or data assimilation, the upper level sources need to be better quantified. Disclaimer: The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2007
- Bibcode:
- 2007AGUFM.A21A0014N
- Keywords:
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- 0365 Troposphere: composition and chemistry;
- 0368 Troposphere: constituent transport and chemistry;
- 3315 Data assimilation;
- 3355 Regional modeling